Livestock often undergo significant exposure to transport and handling, co-mingling, auction and some time off feed and water. Collectively, such animal management events can impede the animal's immune system, impacting its welfare and performance, and creating significant health, environmental and economic concerns.
Treating livestock diseases depends upon the ability to detect, diagnose and treat animals early, and is only as effective as the information available about the animal and the reliability of that information. Most diseases are not detected until clinical symptoms are observed, by which time transmission within and between populations of animals, or between animals and humans, may well be established. Even after a problem is recognized, it is often too late to halt the spread of infectious disease throughout a herd, or to prevent the transmission to other herds, or humans. Early, accurate and effective detection and diagnosis of disease are key to disease management and treatment.
Many current disease detection methods require that the animal caregiver observe the animal on a daily basis to detect abnormal behavioural patterns or clinical signs of a non-steady state such as illness (e.g. decrease in eating due to loss of appetite). Observation methods, however, are unrealistic from both time-management and economic perspectives for a producer to regularly inspect individual animals, particular in large agricultural operations. The best that can be relied on are knowledgeable barn staff, diligent in spotting the behavioural signs, which is inaccurate, impractical and far from ideal. Further, traditional clinical signs of disease provide poor diagnostic results because clinical symptoms often occur late into the course of the illness.
More accurate diagnostic techniques are known, such as the use of acute phase proteins or hematology assessment, but they require the capture and invasive in vivo collection of biological samples, which result in the significant cost of analysis and time. The requirement of the capture (and therefore restraint) of the animal in order to collect a biological sample causes stress, and the process itself is therefore introducing inaccuracies into the data collected.
Recent research has focused on alternative approaches to non-invasively determine the early identification and onset of disease in livestock. One such approach is infrared thermography (IRT), which can be used as a means of detecting the dissipation of heat in animals without having to come into contact with the animal. Thermography operates on the principle that infrared radiation can be utilized to observe radiated heat loss and to provide an early indicator of fever because up to ˜60% of the heat loss from an animal can occur in infrared ranges. The technology has been demonstrated to be effective in non-invasive identification of transport and other environmental stressors that can alter an animal's heat loss. Importantly, changes in radiated heat losses can be detected several days prior to the onset of clinical symptoms.
IRT information from a hand-held camera has been used to predict illness in animals late into the disease (e.g. two days prior to mortality within the group). However, taking thermal images with a hand-held camera compromises the precision and accuracy of the measurements with large variations in camera-to-subject distances and angles. Known IRT techniques thus prove impractical from a disease surveillance perspective because they require the camera operator to visit the same pens at least daily, and much more frequently if the efficacy of the measurement is to be optimized. These methods also fail to accurately obtain images of groups of animals, due to losses in sensitivity when changes in the temperature of an individual animal are masked by the temperature of the group. Environmental and other factors, e.g. metabolic responses to feed consumption, or circadian rhythms in body temperature are also not accounted for, resulting in data being skewed by the group's surroundings e.g. floor, walls and the inclusion of irrelevant information.
One method to improve the accuracy of IRT information in animals has been to combine the IRT data with behavioural biometrics for the early detection of non-steady states in animals. In PCT/CA2012/000279, IRT information was measured in individual animals and combined with behavioural fidgeting information to detect non-steady states in those animals. Fidgeting behaviour, however, is observed in individual animals and cannot be used to accurately and effectively detect the health and performance of groups of animals.
There is therefore a need for non-invasive, early and accurate means of identifying biologically important states, such as non-steady states in individual animals within a group. Furthermore, there is a need for a non-invasive detection means that are capable of identifying diseased animals, even in populations where there may be a low prevalence of the disease.